{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt',header =None, sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns =['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','createdtime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
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       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
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       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
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       "      <td>2018-11-01 00:01:07</td>\n",
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       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
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       "      <td>2018-11-01 00:02:07</td>\n",
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       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3      162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4      162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval          createdtime  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
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       "      <th>96995</th>\n",
       "      <td>7220576</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>83.24</td>\n",
       "      <td>83.24</td>\n",
       "      <td>83.24</td>\n",
       "      <td>83.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-25 10:56:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11093</th>\n",
       "      <td>1154196</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>537.84</td>\n",
       "      <td>113.59</td>\n",
       "      <td>167.09</td>\n",
       "      <td>134.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-13 21:17:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173482</th>\n",
       "      <td>12974135</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>850.30</td>\n",
       "      <td>84.84</td>\n",
       "      <td>282.63</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-24 12:05:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95068</th>\n",
       "      <td>7089945</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>2041.24</td>\n",
       "      <td>105.55</td>\n",
       "      <td>1012.77</td>\n",
       "      <td>291.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-22 19:20:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168906</th>\n",
       "      <td>12616949</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>1532.13</td>\n",
       "      <td>102.65</td>\n",
       "      <td>216.21</td>\n",
       "      <td>153.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-18 23:55:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "96995    7220576  /front-api/bill/create      1         83.24         83.24   \n",
       "11093    1154196  /front-api/bill/create      4        537.84        113.59   \n",
       "173482  12974135  /front-api/bill/create      5        850.30         84.84   \n",
       "95068    7089945  /front-api/bill/create      7       2041.24        105.55   \n",
       "168906  12616949  /front-api/bill/create     10       1532.13        102.65   \n",
       "\n",
       "        res_time_max  res_time_avg  interval          createdtime  \n",
       "96995          83.24          83.0        60  2019-02-25 10:56:37  \n",
       "11093         167.09         134.0        60  2018-11-13 21:17:32  \n",
       "173482        282.63         170.0        60  2019-05-24 12:05:15  \n",
       "95068        1012.77         291.0        60  2019-02-22 19:20:32  \n",
       "168906        216.21         153.0        60  2019-05-18 23:55:09  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   api           179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   res_time_sum  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   res_time_max  179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   createdtime   179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2018-11-01 00:00:07</td>\n",
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       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
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       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "2      162742      5        845.84        136.31        225.73         169.0   \n",
       "3      162808      9       1305.52         90.12        196.61         145.0   \n",
       "4      162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "   interval          createdtime  \n",
       "0        60  2018-11-01 00:00:07  \n",
       "1        60  2018-11-01 00:01:07  \n",
       "2        60  2018-11-01 00:02:07  \n",
       "3        60  2018-11-01 00:03:07  \n",
       "4        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop('api',axis =1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['interval'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-04-07 18:30:23\n",
       "freq                        1\n",
       "Name: createdtime, dtype: object"
      ]
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df['createdtime'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.createdtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
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       "      <td>2018-11-01 00:00:07</td>\n",
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       "      <th>2018-11-01 00:03:07</th>\n",
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       "      <td>90.12</td>\n",
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      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "createdtime                                                          \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "\n",
       "                     res_time_max  res_time_avg  interval          createdtime  \n",
       "createdtime                                                                     \n",
       "2018-11-01 00:00:07        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   count         179496 non-null  int64  \n",
      " 2   res_time_sum  179496 non-null  float64\n",
      " 3   res_time_min  179496 non-null  float64\n",
      " 4   res_time_max  179496 non-null  float64\n",
      " 5   res_time_avg  179496 non-null  float64\n",
      " 6   interval      179496 non-null  int64  \n",
      " 7   createdtime   179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='createdtime', length=179496, freq=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "df.index"
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <th>2019-05-01 23:55:49</th>\n",
       "      <td>11475363</td>\n",
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       "      <td>1083.97</td>\n",
       "      <td>70.85</td>\n",
       "      <td>262.22</td>\n",
       "      <td>180.0</td>\n",
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       "      <td>2019-05-01 23:55:49</td>\n",
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       "      <th>2019-05-01 23:56:49</th>\n",
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       "      <td>117.31</td>\n",
       "      <td>382.63</td>\n",
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       "      <td>2019-05-01 23:56:49</td>\n",
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       "      <th>2019-05-01 23:57:49</th>\n",
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       "      <td>101.71</td>\n",
       "      <td>193.80</td>\n",
       "      <td>147.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:57:49</td>\n",
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       "      <th>2019-05-01 23:58:49</th>\n",
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       "      <td>84.43</td>\n",
       "      <td>347.56</td>\n",
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       "      <td>2019-05-01 23:58:49</td>\n",
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       "      <th>2019-05-01 23:59:49</th>\n",
       "      <td>11475664</td>\n",
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       "      <td>103.58</td>\n",
       "      <td>206.57</td>\n",
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       "      <td>60</td>\n",
       "      <td>2019-05-01 23:59:49</td>\n",
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       "                           id  count  res_time_sum  res_time_min  \\\n",
       "createdtime                                                        \n",
       "2019-05-01 00:00:48  11406128      6       2105.08        125.74   \n",
       "2019-05-01 00:01:48  11406236      7       2579.11         76.55   \n",
       "2019-05-01 00:02:48  11406347      7       1277.79        109.65   \n",
       "2019-05-01 00:03:48  11406446      7       2137.20        131.55   \n",
       "2019-05-01 00:04:48  11406488     13       2948.70         86.42   \n",
       "...                       ...    ...           ...           ...   \n",
       "2019-05-01 23:55:49  11475363      6       1083.97         70.85   \n",
       "2019-05-01 23:56:49  11475483      4        840.00        117.31   \n",
       "2019-05-01 23:57:49  11475550      2        295.51        101.71   \n",
       "2019-05-01 23:58:49  11475597      2        431.99         84.43   \n",
       "2019-05-01 23:59:49  11475664      3        428.84        103.58   \n",
       "\n",
       "                     res_time_max  res_time_avg  interval          createdtime  \n",
       "createdtime                                                                     \n",
       "2019-05-01 00:00:48        992.46         350.0        60  2019-05-01 00:00:48  \n",
       "2019-05-01 00:01:48        987.47         368.0        60  2019-05-01 00:01:48  \n",
       "2019-05-01 00:02:48        236.73         182.0        60  2019-05-01 00:02:48  \n",
       "2019-05-01 00:03:48        920.52         305.0        60  2019-05-01 00:03:48  \n",
       "2019-05-01 00:04:48        491.31         226.0        60  2019-05-01 00:04:48  \n",
       "...                           ...           ...       ...                  ...  \n",
       "2019-05-01 23:55:49        262.22         180.0        60  2019-05-01 23:55:49  \n",
       "2019-05-01 23:56:49        382.63         210.0        60  2019-05-01 23:56:49  \n",
       "2019-05-01 23:57:49        193.80         147.0        60  2019-05-01 23:57:49  \n",
       "2019-05-01 23:58:49        347.56         215.0        60  2019-05-01 23:58:49  \n",
       "2019-05-01 23:59:49        206.57         142.0        60  2019-05-01 23:59:49  \n",
       "\n",
       "[884 rows x 8 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =df.drop(['interval','id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:06:21</th>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:07:21</th>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:08:21</th>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2019-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:09:21</th>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:10:21</th>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179496 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdtime                                                            \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 23:06:21     11       2783.48         99.24        489.90   \n",
       "2019-05-30 23:07:21     10       1951.10         85.37        529.51   \n",
       "2019-05-30 23:08:21      3        494.17        103.95        211.47   \n",
       "2019-05-30 23:09:21      9       1798.28        101.11        433.30   \n",
       "2019-05-30 23:10:21      6       1017.97         74.45        298.97   \n",
       "\n",
       "                     res_time_avg          createdtime  \n",
       "createdtime                                             \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07  \n",
       "...                           ...                  ...  \n",
       "2019-05-30 23:06:21         253.0  2019-05-30 23:06:21  \n",
       "2019-05-30 23:07:21         195.0  2019-05-30 23:07:21  \n",
       "2019-05-30 23:08:21         164.0  2019-05-30 23:08:21  \n",
       "2019-05-30 23:09:21         199.0  2019-05-30 23:09:21  \n",
       "2019-05-30 23:10:21         169.0  2019-05-30 23:10:21  \n",
       "\n",
       "[179496 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 表示接口的使用情况，大部分都在10次以内，每分钟调用的次数分布情况\n",
    "\n",
    "df['count'].hist(bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#凌晨的时候无人访问，下午2-3点第一个访问高峰，晚上8点左右，第二个访问高峰"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用count重采样，用一个小时进行采样\n",
    "df2 =df['2019-5-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdtime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "createdtime                   \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 =df2[['count']].resample('1H').mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u7bq/OvBe0uGKd/ZwtnreX9mRe17nthhyfdbZl1JF9fIN2Az4dv46uWv5/qRfVrNKZK8LXEqanDe9a/mrgKuA9Xoxu4G+qOfN166ej56/OvAj4IjuHpDOwXYj6ZBtL2ar5/2VHbnndW6LIddnnX3p5wn8vyLtHTiCNF8GM5vi7t8mzaHZsWiwu/8eOAPYHdjezFY1s6nufhHwO9LIvOeys9r6Umd25J7XnK+ej57/R+AY0mGE15rZi8xsWXe/EvhfYMsezVbP+ys7cs/r3BZDrs+cX0tf+n3O2DTgQ8A+pF2UPwFWAo4DXurui0tkTyVNkt4H+ClwP+DA+3L2H3sxO+fX2Rf1vPna1fPR8yeTDim8hnQSxmXz17fm/EJzdBrIVs/7Kztyz+vcFkOuz5xfeV/6cjBmZpPcfYmZLe/uj5nZJsDhwDPAVOAadz+tZPZqwAOkXZb7AUaaMH2Vu5/fa9kj8uvsi3refO3q+ej565HegKeRPlk1FZgDXOnuV/RwtnreX9mRe17nthhqfY7Ir36djnX8sh9uwEeB9bvuT68wewGwaU1115bdQF/U8+ZrV89Hz383sGXAbPW8v7Ij97zObTHk+qyrL321Z8zMNgb+Qvpl9Gszm+vut+c5NM+Y2Rx3v7Ng9jzSHocZwJXATHf/c1f21u5+vdnEr3NVZ3bOr7Mv6nnztavno+e/EpiS87/j7s/m5Z383dz9vyaa20C2et5f2ZF7Xue2GHJ95pxa1yn00WFKMzuG9GmJlUjnEvkrcIG7/zg/viuwrrufUiD730i7Oh8mnUdkFvBNd784P74daRT+1V7Kzv++zr6o583Xrp6Pnn9szr+OdL61TUnX4/xmfnxzYBd3/1KPZavn/ZUdued1bosh12f+97Wu07/xmnbjNXkjHau9j3RV9tWB3YCjSGfG3Tv/zDrAagWyZwF/AFYEpgMbAoeSJkofSTrB22xgpV7KbqAv6nnztavno+evCNwGrJ3vL0uauHs58HnSwHUGsHyPZavn/ZUdued1bosh12cT6/TvnqtsQC/cSJd8ObuzQvKytYCDgfOAdUpkTyWdaXeLrmXLAzsD3wI268XsBvqinjdfu3o+9nN8ihEnuSVN2P0q6ZInPZetnvdXduSeN/C+GG59NrVOO7e+OM+Yuz9EOtfS6Wb2xrzsbnc/lXQNqn1LZD8F3AqcY2ZH5GWPefpExq3AwWZmvZads+rsi3refO3q+dh+DpxmZsd1Pe+defnbzazMe10t2ep5f2VH7nkDtYdbnzmniXUKEH8wZul8Irj7MaTDNTuY2efMbI/8I5sBTxbMnp6zTyRdmX0zM7vAzN5oZsuQRsd/8Dxc7pXsnF9nX9Tz5mtXz0fPn5Xzv0W6Rtw6ZnaTmb3d0gXU9wWuc/clPZatnvdXduSe17kthlyfOb/WdfoPz1dRTivMbC/SxLqXAl8B7iadV2Qu6YRsfwYedPf9C2TvA+wNbEy6rMzt+TYEfDB//5i7H9ZL2Tm/zr6o583Xrp6Pnr8fcABpLt1PSHNHLiRdn/PDwM3AEnc/ssey90E976fsfYjb89pqj7o+c/4+1LhOR33OqIMxM5sC3AW8hTRv5mXAI8C1pAsnP2xmc4B73f3pAtn3kCYCrkba67AK6SRvp7n7PWa2irs/WLDuWrK78uvsi3refO3q+ej5d5POsj2N9MGFrUkTbb/g7reY2XLu/tcezFbP+ys7cs/r3BbDrc+u/NrW6Zi8oslnTd9Iewku6ro/hbQr8WzgUyWzNwLO67o/ifTJtROAL/VqdgN9Uc+br109Hz1/DeCsrvtGetP8v8B/Asv1aLZ63l/ZkXte57YYcn02sU7HfN66guu+ATOBC0jX5Fura/kQ8ENgXonsacB384rduGv5aqSPzL6mF7Mb6It63nzt6vno+VOA04HvAS/pWj4DOAuY36PZ6nl/ZUfueZ3bYsj12cQ6HesWdgK/uz8KHEs6x8d+ZrZ93nV4B+lcTC8ukf0E8HbgXuAQM9vfzNZ09/vzsrm9mJ3z6+yLet587er56PnPuPuBpDfHfzGzo8xsyN3/AjxKOj9QL2ar5/2VHbnndW6LIddnzq91nY4l7JyxDjPbhnTYBtJ5mJaQPlkx7O6FPl3Wlb028DqeOyb9IPBCYLtezs75dfZFPW84Xz3/h1xzd8/zO/YkHc7dizSRd11gh6L5dWZ3PYd63gfZXc8Rrud11h59febnqfX3xT88X7TBmFk6p0deGduS9g7cT5rYvITUuEXufnPJ53k5qfk3AxsATjqW/DMvcN2/zgZUR3YnH+rpi+Ur1efvw/S8ieyuN4Za1mlN67O2bXHEdljb+szPtQ/pL+Efkg7nTsv5N3uxScfdfak0uzs/2nvLiOfah4r7Umd2V8/rzK7ttZ+/r7T2pl6j0V6f3c9R9+toNFPqCK1TZ2VkLwYezrsVC18EdAwzgDs9fULtlrzs1qJhI+peocrsUfI3J53qoJK+uPsSSyfO60yUfKCKbDObkXctd8wknbeldF8snWfmwa4X5kzgrqp6bmY7A39091vzm1ul24uZTXf3x/PdzYGHKlyf3dvKSsDdNW2Ls6rOHuEh4P78h8Kf8+2PJfKM9KYL6Tp0f6wq28yW7fprurLtHJ7rd94OZ1LtdvgC0ntsp/aHgMVV9MXMhkmfAr67K/uBirJfBdzn7ou6sivbVsxsZU8nYYaK31tG8QgVbovQ2Gs0zOsT6n2NjovXMBGtjhtp5PvP+XYUsGbXY5Py1wOAKQWybeT3I3PI16EqkL0s6XI1/0I6zLRsVdn53y4HvBv4AHBgXjazor4sT5oouW7XslUryp4BnAysMErvO9lFez6TNLlzi05W12OTK+j58qRd4id2LZta0fYynfSpnZOA93eWjai9aM+nka6r9hbg/cAao/S+TN3HkOa3fRmY2/VYqfXZ+b+zlOvLAZ8Eli2QvRzwXuCj+f+wYoXZ03NPPk36EMakrn6U3c5nAN8nXaS40u2wK//bwCqjPNapvWhfZgBXANvUsD6XJ11P8Nvk96uKs08Czge+1P3+UtF7y3Kk9/KjgC8CQxX2vLbXaNTXZ1dfanmNTuQWaQL/Z4BXkM4lsirwfTM7tnMIzdKV2ae4+zMFsr9iZqea2WqeO9+dk48dP+QTPI9T9u/APqRJf9uRPv1RVTbAJ0iHrlYgnZV9OrBK3t1ati+7kn7x32BmC3PmAxVlfwR43N0fMbNpue6turLXJv2VXKQvnwB+6+43AjPMbFMze1veVp4tmQ3wMeBXwCZm9qVc81OdB0vmf5z00e0fkM4ovRLwsq7ay/T8U6SPaBvp5IiXm9n7u3bNl6n7BNKe6p8DfwGuz72ZVMH6hPQL8FQzGzazad0PmNlkM5sB3OLF5nJ8AphHOrfQ6qT5J51sy9m/LJj9cdLFha8jXS9vVWC4or5sAvwTcKaZfcXMZrv7U2a2Wq69bM8/Stpz9aCZLW9ms81sj67aZwA3FezLccCN7n6dmc0ysx3M7Bgzm51rL5P9MeB/SIeZTugclusws5klsycD/0raS7Oumb25wveWT5L2hF9FGhT8wsw+WVHP63yNRn19Qr2v0fGrc6RX1Y30SbKbgZXzfQO2JO21OZm0gpah2N6C9UnzcS4k7Y58L/kvnPz41BJ1b0LaSDr33wNcRr7Ce/fzFMzfmHQMu3P/WtLZgk/KXzciHYqecF9y3nLAEaTB3umkOUvnA2d2elOw5y8kncF4br7/MdJHks8i/TW7YV7HViB7WWAhsFe+/x3ShV4vy/W/srMNFezJ5sBP8ver5O3mXSMzC9a+FvCLrvuLSB+xPp/0RjGP9IugSM/XJL2Jd+5vRbrQ7XmkX4xTSvR8Fuks2Bt1LXtB3gavyNtpoeycNQTc2bUe/5U0j2PZ/Hjhv1jzttj9Gt0XuJp80fUKsn/edf9nwLnAafn77Upui8uQzgb+BtJemluAi4ALO7klsjvvi1vn+yfm1+bFeV1vW6Ivk0m/AA/K988l7QW6BPgdsH+J7E1Jcyk77wWX5OwV8rJJJbJfANwAzM73ryed7+/7wGJgz5Lrc/Wc39kTvjLp9Ao/As5klD2UE8iu7TUa9fXZlV/ba3QitxB7xtx9MemX0qvzfXf3G4D3ka7Ht42nUeuzBeJnAwvc/dWkQzh7A9eY2d758Y+b2W4FS9+WdLis8//4LPA4aQMA+ICZvaRgNqRfzv8BYGY7kgZPR5L2gNwFbOVpD8qE+5L/Kvgrab7CQZ4+Svxm0p6VYTOb52lvUJGev5g0GJtnZh8g/XW/ADiaNGDaKq9jn2iwp7+OLgW2N7N1SJfEeJu7707qzbz8cxPOzl4HfDNnPAh8FdjLzF7ZnVkw/xHgJjPbx8wOJb2BHpSf83Rge3d/1ovtFXsE+I2Z7Zrv30s65HI06Y14sxI9fzjX96quZfe5++tJE2yHi2Zn04Aj3f1tpMOrLwS+AByYH/+ame1bMHtD0oC9U/fZpLkhm+ZFZV7/LwSOh7998OUx0nvMwaRf4tvk5yzUl/yetwh4hbsfDrwT2AmYa2a7l+z52qS9szuZ2WeBHYHDSe+PF5A+XVaIuz9Lmvu4U5439rS7v8vd98zPsbPla7EWsAvpEFznveADpNdR53dHoesUZvcDVwKvz+9bM919X3d/BekP7Zfl5yja8/tJf1B3trdVSOe2ehVpjtQmRQvPr9FvkfuQl1X1Gl0OeE9Nr88XUt/rE9IOi9peoxNS92ivqhvwctIv8DP4+/libwfOKJk9a8T9Q0mj4uuA35XMXjN/XSZ/PZZ0puD1gZ9W0JfOXJ+t+PsT4L0dOLWi3h9HOnR2JOkvtbfStZelYOYOwOdyn1/dtXw+8I2S2WuTBsHX53o7f2nuDFxVw7Z5CGmvxOsqyHoz8AvSnohPdi3fF7i8ZPahpD3MV5D+Iv5QXv5v5L17Jba/YdLA4Hz+fi7afsD3KujLivz9/JzXkvakXkU6JF0me0269grk1+fHSAO1a0tmd+YRbUAa8HaW7w98t6Lt7yjgRaQBwX/mbeWKCnI3y5mXjniNvgk4t+j2km8zSHvwv5/X4yb58S3Je7aqupGmidye/y9F53J1to1Xk/aGH0W69E7n8d2Aq0vU2Mnfj7RH8r/ya/TovPy9wHEl+7ANae9Y5a9R0jSZ7iMDVb4+18hfO/O3Knt9jsjdsK7X6LjqaOqJSjRqLukvhMmkEfhHSLuyP03ao3IDeXd3gez1SceH1x7j8SeBAwpmb5DrXmvE8h1Jf3H+L/CmEn3ZINe+7ojlnRf1JSVqn5uz18v3dwFuAv5E3lVOwUOsXX3pfMhgM9IEyk7dl1ZQ94x8/4OkPUKfJ+1duqpods7bMOcPjfLYQaRDCbMKZq9H12EI0iHgH5AG76uQDlUUOnzTySb9AlyR9Mt6w67Hf1xmW+ze9kiHyx7Mr893kAbEhV6fY2zXk0csf7yK2kc8x4akX1o3ltleRqu3a/n3iq7P7m0kf90B+E3e1ku9Psd4ns3Jf9BUVXvX9vK+3OfPkfZiXVlBz//hMD5pj+FZjPFeX+A5liUdtj2B5+Z4le5JV1/2BlbvWvZT8ge0CuSt3cnK2V+p6jVKmmO1Os+9n4/8AEnh1yfpnGGr89z7+ZT8tZLXZ1f+CmM8Xsl2Pt5bT59nzMzeA+xOOnb+E9Jx+e+QdiUeSzoU97C7f75E9izSwOgx0ij4+vz4dsD73H2/GrJvynXvONHsMfIfz/nX5cc/BOzs7v9UQfYjpDedLYCn3P28ER8BLpK9Mml3/IOkv7B/0fX4Hp52+5fNvp80d2wJ8C7g96TDId+caPaI/LHW6crAwZ4ORZet/U+kuZCTSfMwnDRPbUHJ7GtIF7s9191/mR9/E7C3u7+xQPa+wJPufuGI5UOkPZx35sdPmWj2aPmWTq+CP3fOu82BD7j7wRVlu3d+Y5ldDExz95dXWbelE1W+G3i5u+/1PDHjyu5avhvpl9Z5ZjbN02lQJmys/K7H55PmY+5dYe0vIA0+7iENiC8om50n7VvXtjID2Mfd/18V2e7uZjYHOJV0yP8Wd//YRLNHyx/xWOfEpvu6+1sKZL+L9PpfD/iOu38kLx8iHT35AwVfo13Zc0lHp44b8fhmpNfnITVkX0yak1boEOXz5edD5EdQ8DVaVM8Oxix9iuxXpOPkM0jHiLchjcRPc/erzWyyp/kHVWRvTTqR5sXuflH+VM+z/ty5ZKrIvsTdL8zzdh7w9Gm/OmrfBnjE3W+rIHuYdBjnh+5+Vv65v518r4LstbrqfjHwmLv/poLsl/DctvI/E8kbZ/7f9bzi7OFc+xnu/sM80PuTT3C+y4jsmflrp+cXufvFZrYu6c34vglmr0raQ/170mk+ju0MqquwtPz8y3YSacD0WFXZXZ+gehFp8D6hcwuNpy/5F+ESn+DJI5dS94RfkwVrX4c0aL2r6uy66rauk1ZXnd1Q/kzSXqGHJ5i9Cmmy+86kPfqfIx1JWlDk9+Y4sq/r/oPRzJYh7Smb6OtzPNlbAk9M9PU5gfz1Sa//Wk7wOqrRdpf1wi036SzyJyjzsiHSvJdz6NqFW3H2mTVmf4euY/U11f6CGrIPI+0dq7PnddR9KGkSZuG6W9xeDsu119WXsj1/K2nPY+ccPTcCnyUfpiVNIZhbY/5uFDzkNI7s3bt7VkNfZlec3fmU+W6MmLZQQ+2r1bg+16sxu9C2OM6eDNXc8zULZn8IWNh1fx3SNJBO9rYl1ud4sotO2Vha9nYUPKfYBPKnF80vXFfTTzjBpp1Imivz6hHLTwLePYjZLdb+lV7uS5/2vGezSYdQN+a5vesbkw6t/oR0rqTfMcZcjArzV1R2X/W8UO1Rsxvo+QbkEwLz3AfIvkuacrJafo4Zg5TdRH7hupp+wgKNO4T0l8OngJflZRcBRwxqduTao2ZHrr3qbJ7nnDukw7fPkD8F1mv5yu6v2qNmN5Gfczrn+up8YvAjpD1Dp5EOVw5cdhP5RW69PGesM0lyRfJpG0hng19MmuPymkHLjlx71OzItdfdlzGec2fgi+6+RdXZdecru/l8ZTebb2YbkK5O8Dt330HZzeSPq4ZeHYyNZGZTPV3qY33ShU0LfVqon7Lrzld28/lRs7ueYyfSB1+urjq77nxlN5+v7ObzzewU4Dx3P1/ZzeUv9fmjDMZERESkmK495TPd/VFlN5M/Xj1zOSQzW8HSRUZnKruZfGU3n6/s5vOV3Xy+spvPX1q25z0vRQYcUbObyK9Kz+wZM7NLSZdU+DrpxJe/9nyVdMvnE7OC53OJmh259qjZkWuPmh259qjZkWuPmh259qjZTeRXpSf2jFmaw7Iy6UKm84B/BvbOyyGdsJKCKzpkduTao2ZHrj1qduTao2ZHrj1qduTao2Y3kV8pb+EjnCNvpHN7zMvfrwAcDnwT+DjpjOE3AIcMUnbk2qNmR649anbk2qNmR649anbk2qNmN5Ff5a1nDlOOZGabAq8kXTl9krtvM+jZdecru/l8ZTefr+zm85XdfL6y28kvqtXBmKWLoB4AGOm6fGe6+4MjfuYx4LXuftkgZEeuPWp25NqjZkeuPWp25NqjZkeuPWp2E/l1aHvO2L+TRqdzSZeB+KGZvb/zoJnNAT5SsFlRsyPXHjU7cu1RsyPXHjU7cu1RsyPXHjW7ifzqtXV8FFgFuJV80VzSNbq2J10EfCEwmzSqnTQo2ZFrj5odufao2ZFrj5odufao2ZFrj5rdRH5dt9b2jHnaZXgOsHO+/6y7/y/wLmAJ6UKe7gU+5RA1O3LtUbMj1x41O3LtUbMj1x41O3LtUbObyK9L24cprwW+YGYnWbp+Hu5+D/Az0sWNBzG77nxlN5+v7Obzld18vrKbz1d2O/mVa3Uw5u4XAC8CpgCLzOzDZrYDcBjw34OYXXe+spvPV3bz+cpuPl/Zzecru538OrT9acrJ7v5s/n4z4N+A24G/uPtxg5hdd76ym89XdvP5ym4+X9nN5yu7nfw6TGm7AAAz2xZ4gbvvbxVfliBqdt35ym4+X9nN5yu7+XxlN5+v7Hbyq9ToYUoz28DMlunc74xcgeOAqYOYXXe+spvPV3bz+cpuPl/Zzecru538JjQ2GDOzrYCTR1m+IfCgu58Nha9vFTK77nxlN5+v7Obzld18vrKbz1d2O/mN8YbOoQF8F/jn/P06wLbAocA6XT9T9LwiIbMj1x41O3LtUbMj1x41O3LtUbMj1x41u4n8pm7NPAnsCtzWaU5u3lnAl4CLgZcOWnbk2qNmR649anbk2qNmR649anbk2qNmN5Hf5K2pCfxLgOuAw8xsfeBpd9/PzFYAPghsA1wzYNmRa4+aHbn2qNmRa4+aHbn2qNmRa4+a3UR+YxqZM+buPwLmA3cBKwOfzMsfAe4Hthy07Lrzld18vrKbz1d28/nKbj5f2e3kN6nW84yZ2XRg93z3fuAXwBPu/nR+fCXgv4D57n79IGRHrj1qduTao2ZHrj1qduTao2ZHrj1qdhP5bah7MHYGYMB04NfACsAVwDnu/oSZHUs6B8g7ByU7cu1RsyPXHjU7cu1RsyPXHjU7cu1Rs5vIb0NtgzEzGwIudfdN8v0tSLsMtwKud/dvmtlkYIq7PzkI2ZFrj5odufao2ZFrj5odufao2ZFrj5rdRH5rvKZPBgAzgUuA13YtWx7YG/gxsH1eZoOSHbn2qNmRa4+aHbn2qNmRa4+aHbn2qNlN5Ld1qzc8Nedi4Ghg7a7lHwKOHsTsyLVHzY5ce9TsyLVHzY5ce9TsyLVHzW4iv41b7RcKN7NdgVcAqwO/Bb6dm/hBd//uIGbXna/s5vOV3Xy+spvPV3bz+cpuJ79pTQzGJgFzgY2BN5B2J/7E3U8Y1Oy685XdfL6ym89XdvP5ym4+X9nt5DettpO+mpkBU939STObCTzl7oeY2VR3f2oQsyPXHjU7cu1RsyPXHjU7cu1RsyPXHjW7ify2VHrS1zxSxcwme9L5JMNpwF8AijYranbk2qNmR649anbk2qNmR649anbk2qNmN5HfCyrbM2bp46VvNLNlgLvN7CZ3/4GZbQ0scvfClySImh259qjZkWuPmh259qjZkWuPmh259qjZTeT3isrmjJnZDcAXgGeBHYENSSdh+wZwl7s/ZWaT3H3JoGRHrj1qduTao2ZHrj1qduTao2ZHrj1qdhP5vaKSw5R5hHqPu5/s7qcCJwL3AJOBPTq7Dwuu6JDZkWuPmh259qjZkWuPmh259qjZkWuPmt1Efi+pas7YPcCKZvZ+SxPqNgKWBb4FvCPvZhy07Mi1R82OXHvU7Mi1R82OXHvU7Mi1R81uIr9nVDIYc/f7gAXAHOCnwIHACe5+M+linS8etOzItUfNjlx71OzItUfNjlx71OzItUfNbiK/p3i5s+BO6vp+KrAaMASskpetB/wSGBqU7Mi1R82OXHvU7Mi1R82OXHvU7Mi1R81uIr8Xb2U/TXm4md0H/NDdFwP3j3j8QOBcd79jgLLrzld28/nKbj5f2c3nK7v5fGW3k99zCn+a0sz2Ac4FPg08TLpA51VezUndQmbXna/s5vOV3Xy+spvPV3bz+cpuJ79XlZkzti5wFHAhMB14DfDOzoQ6M3urmU0yMxug7Mi1R82OXHvU7Mi1R82OXHvU7Mi1R81uIr8nldkzNgNYxt0fNrOVgD2AlwB/AjYHtnb3DQcpO3LtUbMj1x41O3LtUbMj1x41O3LtUbObyO9ZXnCyGTBzlGVrAocDfwbmDVp25NqjZkeuPWp25NqjZkeuPWp25NqjZjeR36u3CU/gN7NjgNnAHDO7B/iguz8O4O73mNmawNXu/uNByY5ce9TsyLVHzY5ce9TsyLVHzY5ce9TsJvJ73kRGbsCWpI+T7gxsDZwOPAC8t+tn1gdWnOioMGp25NqjZkeuPWp25NqjZkeuPWp25NqjZjeRH+E20Ya9Azh1xLJh4EfAJ0nHeYsVEjQ7cu1RsyPXHjU7cu1RsyPXHjU7cu1Rs5vIj3Cb6KcpzwXMzLbvLHD3RcAhpJOyzZ5gXj9k152v7Obzld18vrKbz1d28/nKbie/9xUYwR4K3At8CZjctfwG4P+UHB2HzI5ce9TsyLVHzY5ce9TsyLVHzY5ce9TsJvJ7/TauU1uY2VzS5Qd+6+535Il0J5E+ZnoW6TIFM919z6WG9Ul25NqjZkeuPWp25NqjZkeuPWp25NqjZjeRH8lSB2NmtgZwJrAEeBw4091PzY8Nkybe3Qb82t3vndCTB82OXHvU7Mi1R82OXHvU7Mi1R82OXHvU7Cbyw1narjPgFOCY/P2ewK+AzarYLRc1O3LtUbMj1x41O3LtUbMj1x41O3LtUbObyI92e94J/Ga2Fmk34TcB3P0S4DLS5Qkws7lmtrvZxC9LEDU7cu1RsyPXHjU7cu1RsyPXHjU7cu1Rs5vID2kco9fNgRld97cBTs/fnwe8vcTIOGR25NqjZkeuPWp25NqjZkeuPWp25NqjZjeRH+02njlj5vmHzGwZYDlgIfAbYFt33/15A/owO3LtUbMj1x41O3LtUbMj1x41O3LtUbObyI9mqZdD6jQrf/808LSlSxUcA+xa5smjZtedr+zm85XdfL6ym89XdvP5ym4nP5oJX5syWwj81d1/VGEt0bPrzld28/nKbj5f2c3nK7v5fGW3k9+zxnWesVH/odkkd19ScT2hs+vOV3bz+cpuPl/Zzecru/l8ZbeT36sKD8ZEREREpLyJXptSRERERCqkwZiIiIhIizQYExEREWmRBmMi0tPMbBcz277Av7vDzFY1s5XM7F+6lq9pZmdXW6WISHEajIlIY8ysyOl0dgEmPBjrshLwt8GYu9/j7vuWyBMRqVTR84yJiIzKzN4CvB9w4EbgWeAJYCvgajP7MvBlYDbwOOmyJ7ea2auBDwFTgQeBA0ln5X4H8KyZHQS8C7gVOAmYk5/ySHe/2sxWAc4A1gKuATrXtTseWN/MbgAuz899kbtvbmaHAPsAywMbAifm538z8CSwp7s/ZGbrj1ZzhW0TkQGmwZiIVMbMNiMNqLZ39wfMbGXgM8DaedmzZvYD4B3ufpuZbQf8B+mM21cB89zdzextwFHu/j4zOwn4i7ufmJ/jW8Bn3f0qM5sDfB/YBDgWuMrdP2pmewGH5bIWAJu7+5b53w+NKHtz0kBxGulSLB90963M7LPAW4DPkU5GOVrNIiKlaTAmIlXaFTjL3R8AyHuVyMueNbMZpEOOZ+XlAMvmr2sDZ5rZGqS9U7eP8Ry7AZt2/fsVcu5OwOvy815sZg+Ps+YfuvujwKNm9mfgwrz8F8AWS6lZRKQ0DcZEpAmP5a+TgD919lKN8EXgM+5+gZntAnx4jKxJpD1oT3Qv7BooTdSTXd8v6bq/hPQe+Xw1i4iUpgn8IlKl/wbekOdvkQ9T/o27PwLcbmZvyI+bmb04P7wicHf+/uCuf/YoMLPr/mWkuWPkjC3zt1cCb8rLXgnMGuPfT8hSahYRKU2DMRGpjLvfDBwHXGFmPyfNFxvpQOCw/PjNwN55+YdJhwKvAx7o+vkLgdea2Q1mtiPwbmDYzG40s1+SJvgDfATYycxuJh2uvDPX9CDpgwM3mdkJBf9rY9UsIlKark0pIiIi0iLtGRMRERFpkQZjIiIiIi3SYExERESkRRqMiYiIiLRIgzERERGRFmkwJiIiItIiDcZEREREWqTBmIiIiEiL/j89OLqxLRqn4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 折线图和直方图，可以看清楚业务的高峰在什么时段，柱状图可以\n",
    "plt.figure(figsize=(10,3))#单位是英寸\n",
    "df2['count'].plot(kind='bar')\n",
    "plt.xticks(rotation=60) #用于文字旋转的角度\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析有没有异常时段，访问接口频繁 \n",
    "df['2019-5-1'][['count']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1']['res_time_avg'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1'][['res_time_avg']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-52-46b1193ea45a>:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df2[df['res_time_avg']>1000]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>createdtime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdtime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:34:48</th>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:49</th>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:36:49</th>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:09:49</th>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:10:49</th>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:38:49</th>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdtime                                                            \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg          createdtime  \n",
       "createdtime                                             \n",
       "2019-05-01 00:34:48        1694.0  2019-05-01 00:34:48  \n",
       "2019-05-01 14:00:49        1162.0  2019-05-01 14:00:49  \n",
       "2019-05-01 18:36:49        1099.0  2019-05-01 18:36:49  \n",
       "2019-05-01 19:09:49        1233.0  2019-05-01 19:09:49  \n",
       "2019-05-01 19:10:49        1815.0  2019-05-01 19:10:49  \n",
       "2019-05-01 20:38:49        1077.0  2019-05-01 20:38:49  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df['2019-5-1']\n",
    "df2[df['res_time_avg']>1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2019-05-01 00:34:48\t1\t1694.47\t1694.47\t1694.47\t1694.0\t2019-05-01 00:34:48  定义为异常值\n",
    "\n",
    "df['2019-5-1'][['res_time_sum',\t'res_time_min',\t'res_time_max',\t'res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df['2019-5-1'].resample('20T').mean()\n",
    "data[['res_time_sum',\t'res_time_min',\t'res_time_max',\t'res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gImP8z7lR61XqNNd7E/YNXo4vBSMemJNEU8ptnsLzwNlplj+kqt39z/sR6xQoleS1UDklNcIkri/+mKpVXmMKqjoIWBG13CiJY/dRbYC+7a+mGUh39fBUkgHOhZGzg3u05q3YwOOfbYv7lO1MF3vPx/GZ2Vxbz5zl8UqAEwVxGlO4TkTG+d1L7TNtJCJ9RKRaRKpramqi1K8Bazdl9nr5aOLiCDXJjbWbghswCzv+TT58MCF+59olFz/ZvOdKrpz8vwP43w+nBna8Qhk2c7kz2dk834KgUFsY5izruBiFJ4D9gO7AIuDBTBuq6lOq2kNVe1RVVUWkXlNqs8wgWrE+fgG0ypXl68JPBWn4OKrNr9pgz1NjwnQBj4VRUNUlqlqnqvXA00BP1zqVGs76Pv0Xhas+4bBDghvRUKmXsdBil31LQUT2TPl7ETAh07algIv+0Rh2yRpGIKjGMyhduRL5jGYReQXoBXQUkfnAH4FeItId7902B7gqar2MwkgM9LoOWmaET7ZB/WIH/Cv1nR/HYkduFFT1x2kWPxu1HqXM+jSzLJeu2dzsrNAwGDi1hvfHL6LdDq0zbrN07SZ236VNKPKtBhksiWxj+RLmZVgScgKpYsYsJixYzeFd2gaoTW6MnZ8+9lkQxKL7qNwI+0X1s+e/aLLs248O4ZQ/DQhVbjrWb6njly+PpvczIzJu0/PuT0OT/8inTVNlxoEBU90FNCuUGUvXcu4jgwvat9h4X9nGFO79YEqoNeoz+g0qeN/z/zKEjUWEtSm0fT1m3qqssaiKwYxCCRKkL3qpsyWCiLSFMHe5u3SdhbJ0beGeXFMXrw1Qk2hZVqQH29YIIgWnY1ZNOOmAzSiEgHVoGJXqTRMW5dpLGMdimVEwDCMnyvXFbDTEjEII2MNjVFpDod5u+oKI431SkUZBVVm9IbzkLGHG4wlT72yk83gqBVx5J7nIqVEszbkVZ8udEXba6M2u83aERByTRFWkUXitej5H3vkxUxYX5n4H2fsCl6wJL/TCkXd+HNqxs3HYHz9yIrdYbn9nohO598Ysi1gQ3PHOpKzra4oYqG6OnveE58HminfGLuSKF6pdq9GEijQKA6d7gfSmLwln9N6ID++Pt4B5UVGMUYhjN0quFNoYHTZzWVFywwrxUpFGIZZD/obhmGLfMcV0m1biIxnXYZjKNAo+5jZoGMER15eckR8VaRSCGAg2e2IYwVGJz1NcjWhFGoUEFsTNMAyjIRVpFOJqoY2mzF5WeekQi6VQt+VSriLNW+EurEjN2sIC9v2jel7AmgRDRRoFo3QYOdtdKsZSZd7Kwl6QpVxXGjt/lTPZ08rMi9GMgmEYgVCpLfByK3dFGwXzPjKMbZTy42Djg8FRkUah3Cy7YVQ6VsELjoo0CgnsPjKM4Agz5lezsq2iFxgVaRSCuHmvemlUAJoYRvlgL+byoCKNQoJimpyW/cyIK4Xe12HF0omCElY9dlS0UTDijw0g5o/V2KPFZbdZGJhRMAwjEIp5NRbbSrGqQ3A4MQoi8pyILBWRCSnLOojIJyIy3f9uH5Z8q0mVDuVWC4sC60oxisFVS+F54OxGy/oCn6rqAcCn/v+QsafHMBK4NCauMuSVMmFdLydGQVUHAY1Hai8AXvB/vwBcGJr8sA6cA1MXr3Ume+y8Vc5k19bVF7TfJ5OWFC17Vk1hYQg2bCkuBekzg2cVtF+xqU8/m1pT1P6F8sH4RU7kAlzz8uiC9puwYHXAmpQ+cRpT6KSqibtqMdAp3UYi0kdEqkWkuqamuJvfRc3oaoeurBc/+bkz2WPnF/bw/Wfy0qJln/bgwIL2e330gqLk3vXe5IL2e2P0/KLk/umjqUXtXyhPDirMCII7z6dLnxvpRG6ciZNRSKJeWzJthV5Vn1LVHqrao6qqqsDjF6NdcVh/rxFX7NYsjHLzkIuTUVgiInsC+N/FVxGbwcWlbOHQKrg1SNZnHHcq8QqV8lhGWKrHySi8DVzm/74MeMuhLoZhGDlRbh5yrlxSXwE+Bw4SkfkicgVwH3CmiEwHzvD/lx0tyqulmTMlXCGLHutjNHKg3LyPfqyqe6pqa1XdS1WfVdXlqnq6qh6gqmeoaohxJIp7Q40pwotn2pJ1BXvUFNvUrat392YeOM2NR0wpsmZjYZnTiqUSTdHKArPUpbJ2U3HeYoXy8vC5oRw3Tt1HkVOox8OFjw0tSu6V/1dd0H6j564sSu7WOndG4S/9ZziTXTCOmjeuvIdcsmlrXdHHWLqmsLSYxXJ3gV5mxbJg1cZQjluRRqFUuzI2bS3M198wKoE6Rw92sfNZ4kZFGoUEpdZcrnXY/WMYccdVZc+lR2EYVKRRKNVXa6Gzgo0CKbOHvTmchrmIyTEKwYyC4QxrKRhGZpzNOSgvm1CZRqHev3meGTKrpCavLAxpYCkqVm3Y4lqFvNhaW3zLbPm6zQFoUjoUErdp09Y6bnjly6Jll9CjHGsq0igkAoYNn7WCITOWOdYmd+54Z5JrFYqiz4ullcL04f9MK/oYpRVbp/gq781vjs97n3vfd+O9ExRl1lCoTKOQympHPuGVyNzlG1yrkBdrAvA/n7NsfQCalA6FtGYXrQ7GldRVS6Flmc1IrXijYE1OwwiOcgsOV4mYUXCtQAVRbjFijGCwuyJemFGwpkJkVOKprrgiO3VrdXO2zSXVKGlGz12ZtyFcsT4Yr6GKe0EWQKl7mLnEVaVjXQEeV1MWrwlBk2Awo5AnLtNpBsF3Hx/G34bOyWufY+76JBDZNWsryz2zEE64r79rFYpi5Oz841hWzwkm9mWpVDrq6pWzHx7sWo2MmFHIk2Vl4Hc+fWl+hq0Su33ixh67tgldhqtekCAilULpdAXXx1zPijcK+V6fmF9Po0xpv9N2rlWIPaXyaMb9HWJGoWRupeCI+01ZTgR1rkulFuwSO0XBYEahAm+kSiyzUQmUxo0d94poxRuF2jwTz5SD91ncb8pyYmMAyWOiotRbIxu2lM65jjMVbxR+9/q4vLYP6rkZNrN0Yi654oPxi1yrEAj//nJB0cc4bt/dAtAkO9974vNAjjNvhZtwJt95tLiMiMWQTyDAuNveijQK3zt6L9cqMHFBfP2U40IpBSvMxogC3DQb84fzDglAk2iYvzKauRYuWu3PXd4j7fJyaqVUpFHYu8MOrlVwm9Ak5jWVBCWiZrMEca1btyydRzWq7slrTt0vEjmp7LbT9mmXl1OXbOncaYZhGEbotHKtQGNEZA6wFqgDalU1fXutGBlFBGgJqkYgDpsKpVKnKfWBzwRl4JuQH+Vx2SqW2BkFn2+qanl0KGfA5YuiVN61paJnc5SDx1o+RHXZSvW8xv2+tu4jR9z5bn5Z1N4ZuzAw2a+Pnh/YsUqFP/9nel7bX/CYO0+WIJi/Mj8PoI8nLg5Jk/Do3K7p2OBLw78KVebObeJajw6OOBoFBT4WkVEi0qfxShHpIyLVIlJdU1PjQD03PDVolmsVIqfQGtVuaUJCPJRnas2x81blLffUA6s46mvtmiwPKvHMQz88MudtJy3Mz7vt32OKd5tNEEVN+KlLjqF3z681Wf7i5+EZhRd+3pP9qnYu+jiFdkE/fWkP/tHnuKLlN0ccjcJJqno0cA5wrYickrpSVZ9S1R6q2qOqqqogAVE2O9++7sRAjlNK3g37Ve0UyHEKLXOrlm76FY7cqy19Tt43tON/vUu7nLfNd8yq1DKmnXXYHmnLGGawuVMPLOx9ExRnHtqJb6TMV2nTOpzXd+yMgqou8L+XAm8CPd1qZORLUI9l3Ptec8VF37fTMSuHFRhnEUgdiA3LkMfKKIjITiKyS+I3cBYwIXA5QR8wAsrlBZkPhRa51Gq9YVCp82DqK/A5CZq4jZp0At70m4WtgL+r6oduVWqIq1DblXizV6IhzEY+L/pS9cwplrjnKoD439exaimo6ixVPdL/HKaqd0cht2vf96jL4a1bX69c+tzIvI6d7aj5+OEH7bO/ta4+p+1yOS+N6bhz+lmfUdFxl+JyDywoIiVmm+1aNlkW1Pt5uzxmNf/8+Wo25hF6Yc2mYBLdQH4tvOeHzg5MLsBXy3P3uirk3s5Ez3s+zXnboDIQ7pjmXguCWBkFl+QSzXJLji/SVLK9zF3W/tdszO0lsGFL/vlnn/jJ0RzbtX3e+xVL57ZtuOW8Q3jusmPZrlXht3Yx7pm90gxGFjtRcfjvTwdg7w470qGRZ9U7151Ej33Sn+t5ebiljl+wOm+9Duu8a9rl+VRgbn8nP9fsIMknt3L3vdsFJreQmF6vXX18k2UXHdUlCHWaUJFGIcqmdakPuhbyQttt5+157eoTipad74DlsN+fzi9O3pfdd23DPh12LFp+Yw7vkv4lmEQklJnqe7Tdlopz9K1nMue+8xro9K9r0p/rfDRpUYDe/772RPZN42kW896RgvjZiV0DO1Yhrf5ju3ZosqxliwoYaC5HAsu8FfCjluvRSnXmdV0RO8e9zzdXwq78qGa4P0rl/DnSM7AegpCurxmFPCjsZZF5p/zGFAqRXTyF1CDjQBjnq1CvJnenMHfBQapYKnNq8tEzlxZgrs9zUIPhFeGSGhWFNvELuZjBeR+5edDcujYWXuZiBhEz7RlH+5jtXg7bW0lL5vUfDbnerkE9ymHdjxVpFNKRywvoiznFJ0tJZX0e3iEza9YHKvuXL49m+pK1zW436quVgcrNlWEzlvHvMYXHe0rX33rF818wY2n2Mo+YtZz/yRCXqrk+3Eyrc63R/d/nc3LaLh2t08ziDtuGZXpkVD0Pmz7/V83qHB0aXND9zk8CPV5tjhWRoCp4LUOyChVpFAqtgV7+ty+yrr+4R8OMblec1I2jv9aeR358VNrtP5oQfhCy5392bNrlI2ev4Lf/aj4V6SXPjshL3qF7bhuMfff6k/LaN5Xez2SXe/4Re2Zd/9zlTcv96ZSl3Pha9jL/8KnhGdc9f3lPBtzUq8nyv/70aH563Ne4ssgQF7e9NbHgffvf2Kso2YXQpnV6l0hVeHLgTD6etIR/fDE3cLmvZxhYD5NzDt+j2W1mLVuX07EyvX7ShYfZt+NOTULlPHnJMbRsIVzdK5wkQxVpFNLGTMnf27QJB+3R0Dvl1vMPpUUL4TtHduYb3Zp6D+Ras2iOmfecm3Fdr4N2b+Ctkkou3guZuifOOrQTXdJEqdxp+20visO7tG32+IXyaO+jmXPfeRnL1q1j+vhL64rwx2+7Y2u6ddypicyzD9+Tuy78Ojttn34uaBTdTnun8bbKr5s087aZXE+z0XaH1gCBtxTevf4kjsnggpsP+VQMrzy5W06Z73J9h6RrKfyy137c8Z3Dmyzvf1MvjtirXYNl3zpsD2becy47Z7jfiqUijUI6aoOwCllI93zWBSSz0HdOGM3PuHvu5OObHhSuhiLyMglZNs637qJ4BhSCNwpBEeTEtQS5dgtl2iouIzRmFHyKcWFMkO0hTNevHFRLoVCKtQlxHHhtjvWbyyfBenPkc32CNua7tkkYhWCNcFB6BvG8NyZXo5Buuzg9SxVpFNLX2qN/QdfWBZXas7D9wpj8Eo+6TmactBQcPfBBuSzmOwanqsmutFxnzkdNPs97rt1wuR4y7q3pijQK6Uh9Qb/4+RwGTF0a6PHT3Vd3vz856z4bt9TxyKfTGT5reaC6JMg2B2HNpq088un0wI3lBY8NzTnuUrnw9OBg4/vkyil/GpDTdk8PmsWydcHE4wHo8+IottR61ziu3Uf3fzAl8GNe8fwXrNqwJes2Uxav4eE8Ez5FTUUahR8d2zRjU6JJ9+nkJdz61kSuenEU4+avavZYR+7djnu/+3UuOqoLF3TvnFx+TSPPgJvPPSQvHWfVrOPCx4bS75Np/CiNR0zjQd5eB22LuXPeEXty5wWH8YNj9mq8W04Mnl7D2Q8Not8nmW9eEXjgB00zgd12/qFZjz123ire/DJ7lq9MwdmuOnVfWrYQvnd0YeUqht+ccWCD/3/wr2e/i3PPhlYIN511YPMb+Zx8QMe8j79g1caMlZOD99iFgzrtwp9/1NR77sxDO2U97vPDPENYTKC9b3TrwPeO3quBR9sBnYrPfAbwQoYMbT8/sVvy9x+/7d3Llx6/T4NtHu19FGccsnuTfZev38KfPpqaVe7ZDw9ma5oegp98Y59ABtCDIG6hsyOhcVAx8Pr3V23YQt83xnNgp51Zv7mOq18cxdvXn5Q16ufjPzmaLu124Md+asBM3jCHd2nLKQdWMWha8ylEPxi/iN/+axytWwr77LZj2siPQ/ueRte+7wFe8/b5n/VM/n+s99HNygCStbkE6zbXcs/7k/n7iLnsV7UTO27Xkg1Z5lIcl5IFKlO5c5HbmHTdav+86nh6duvA78/Jz7gGwd4dduBXZxzQYNmVp+zLlaeEl2UN4GsdduS60w5ofkOf0w7encHT8wu29urIzC6jH/76lIzrksHYMjQkN231rnG+3UeZ7qPEvd3YDXbPtm1YtHpTXjKycdu3D+U5P3Lrz07sxs9SjESC84/ozPlHdE7qVCyZyvzrM3K/9kFSkS2FdNTXK398eyIr12+h38XdefKSY1i+fgvXvjw6sO6OdJ01tSnH3lpXz13vTuKal0ez/+478+4NJ3P6wdlrZMWwqXbbC3/YzGWc/fAgXhk5lz6n7Mt7N5xM+x2LC0Gdieb6qF2M72TDVdKesL1RttbV8+oX80I5dsKJIuzuoxiNzwJkdEsuJcwo+Lw7bhFvjVnIdaftz+Fd2nJ4l7bc+92vM2L2Cu59P5j+x3Rd+Ilw3EvWbKL308N5ZshsLjt+H/551fF0abcDB++xSyCy07Gltp4NW2r541sT6P30CFq3bMG/rj6em889hDatW7J9lvDTxbwom3vVpTMKLr0zXMkOe0Dyk0lLCo7t35xuCXfrdF0l5UxYOQ6ipPTNWkD8+dPpHN5lV6795v7JZd89ei/GL1jNc0NnZwybXGzym81b6xk7bznXvzKaDVvq+POPunNB921x0g8K0ShMWbyWsx8ezLyVG/j5id347bcOYoeUm7qYnATZqG+mJRD2nJF8iVttNCheGv4VXdrtUFRSoUxE5W4dRqjyYthpu9J/pZZ+CQLkwR90bzJz8eZzD2HyojX8/o3xRR8/3e1757uTeGvMArp13IlXrjyOAzo1NAIHdgrPKCR49crj+EbK+ECCbC2FYnh33CKWr8/spZGuyyHIR/+Bj6bmGSyueOkPfpx9ADII0mmZSe6W2nqGzVzOb791ULODo4UwKyVWVxRlTyXf6xskg6bXBJrFzgVmFHxuOe+QtLXy1i1b8Gjvo/nJ0yOYmiaAXD6pJy8/sRsDpjYcaH577EK+c2Rn7rro62mnre+wXUu6792OMfNWJZfts9uOSZ3vem+b58jN5x7MPXl0dZ2w3248c1kPdsxQuzmsS1vGzm+Ykatntw6MnL2CS3yPjPY7tuZHPZt6c2Wj+quVjJ6bX6C9/XfP7HWy+y7bN/D8SnDb+YdyZ5rgdo9/NiMv2Tecvn/zG6XwzYOqmlznxwbkJxOg7zkHZ1z332cfzAONXra9DtodGmUyyya3apft+eGxezNoWg0jZjcM9pjq8QNeGIYnBs5MdhslPGVuOP0Afv2PMVx/2v78pX96WYWUvTG/O/sg+n3c1BvuN2ceyE2vjW2wLN/rmzyW72F2QffOzFjafByjzm3bsLDRIPfQGcsYWkBmtQR77NqG/XffmSEzlnHO4dnje4WFBJ37N0p69Oih1dXVrtUwDMMoKURklKr2SLfOBpoNwzCMJLEyCiJytohMFZEZItLXtT6GYRiVRmyMgoi0BB4DzgEOBX4sItmnxxqGYRiBEhujAPQEZqjqLFXdArwKXOBYJ8MwjIoiTkahC5A6vXK+v6wBItJHRKpFpLqmpvmQEYZhGEbuxMko5ISqPqWqPVS1R1VVVfM7GIZhGDkTJ6OwANg75f9e/jLDMAwjImIzT0FEWgHTgNPxjMEXQG9VzZjNXERqgPQxcJunI1D4LJPicCXbylwZsitNrkvZpVrmfVQ1bVdLbGY0q2qtiFwHfAS0BJ7LZhD8fQruPxKR6kyTN8LGlWwrc2XIrjS5LmWXY5ljYxQAVPV94H3XehiGYVQqcRpTMAzDMBxTyUbhqQqUbWWuDNmVJtel7LIrc2wGmg3DMAz3VHJLwTAMw2iEGQXDMAwjiRkFwyhhJG75KEOiUsoZB8wohICIbJfyO9KbWUR2TvkdiWzx2DcKWRnknyYiO0UsU0TkKhFxkh5LRO4WkUM04kFBEemSuL8jvrdbp+gQ9TPVNiGzEoxTWRoFEblSRB4Xkf0ilnuJiHwOPCwivwGI6qEVkZ+ISDXwJxG5MyrZfsjzj4DnRCTSYFR+mUcB3wQiS4wrIt8CpgAnANs1s3nQsnuLyCDgl8BPI5T7QxGZADwEvAiR3V8/9q/x3SLyq6jk+rK/JyJfAY8Af45Ktoj8QkT+KSInhy0rLapaFh+8vOUtgR8C04FBQG+gTQRy2wC3AwOBk/BeUoOA0yKQvQNwKzAAOAU4DC9EyOERnfftgM/88l4EtIqgzK2Bm4CVwHER32et8F4Q30qnW0gyWwBtgSfxDPCJwC+AG8OUmyL/WGAYcIL/fzJwdATnuocv9zi8kA5jgZ9HVOYq4GNf9g5ANZ4hbhmy3G8Bk4DXgd8B7aMob+qnLFoKItJGPeqA0cA3gCfwXpKHRCB3EzAO+K6qDgGGAEOBThHI3gi8qarfVNVBeC/p6YQUTFBE2qT8FvVyX7wDvAFcAewehtyEbL/MW/HiZL0MfCUi2/m1us5hyU38VtVa4CBgnt+tcKOInOmfi8BrkSKyg6rWq+pq4ClV/ZaqDgUUuNjXKQy5bVL+dgOGquowEekETABWBS0zjdxDgE9VdbiqLsO73veISNswytyIemADsMp/xn4FfAfoHrLcL4HTgEfxgoKeCtG1jqAMuo9E5FbgQxG5XkQOU9XpqroC+BderfJkEWkfotwbRORAVX0DWCUiLfyX1hHA2qDlppF9uKpOEJEWInI68BLei7mfiNzkbx/IdU6Re52IHKGqKiJdgDPwmtiLgItF5EIR2SUImWlk3yAiBwIf4OXf+ACvInAR8IKI/MHfPpQy+4tn4NWe38SrUd6M12UYRpk/8Mv8dVUdlVKu14HaFJ2Clps41/vgVXj2EZHX8FqhAjwjIvf72wfSz95I7t7AVOAc2ZaBsR5YA/zG3z6w95eI3CEi56Us2hFYDrT3Df5QvBr8D4OUnUbuclVdjNfrsADoISJd/W2jGc+IqkkSUlPr53hdF98A7gT+DXRNWX8W8AJweqP9imqKZZOL172wA/AWsFeUZcZLY9rB/30QsA7oGKLcff11d/vftwGbgP8AO4ZY5reAPfFqkv+bOM/A4XhdSruFKLcDcCPwKdu6b7oAI4EjQ77O+6Ss74ZXAege8v31FrCHv+4e4NIU+YuALiHK3QXP4D4PjPKvdQ+8rpydApLbAW9m8Eq87qnWKev+BNydeIbwQvvPAXYPWW5iUvExeF2VvwjyGjf3KdmWgm819wYeV9UReDfMBODexDaq+jHeRfy6iJwnItf6ywtuimWRe49/7Fq8/t+dVXW+iBwpIr0LlZej7Pt82ZPUayWhqlPxunWK7s7JIHcicLuItMbLpz0IOBt4G+8FualYuc3Ivl9VJwO3qep8AFWdAHyI1/8chtzJeOf6L0AtsL2I7KiqC/C6s7oVKzeL7OR1BlDV2cA++N0ZQdRcs8h9yN9kJ7zackL+MODAkOROBh5W1Xvwum6uUNXf4YWKHgZsCajmvB74t6q2x6uZ/1fKuseBrwMn+V2X84DBeBWSUOSmdkOq6ii8VnBnEblcRPoGILdZStYopLzYL/X/r8OzqvuJSK+UTT/Eq208TQCeIlnk7i8i3/TXHQu0EZHbgedIcacLSfa+qWUWkVYi8giwK55RDEPuw3iD2ofgjd+8q6onAJfhvaj2bnKg4GQ/BBwiIr3UG89BRFqLyF/wylxojo3m5D6IV3s7GK8WWQXcIiL9/GWji5WbRXa6e/s14Ex/m/qQ5D4MHCAihwFLgVtF5CwReQCvhTQhJLkPAkeIyGmqulpVx4jnCnsrUKeqW4up3KXI3oznJAHwR+BK8d2MfcP3d+Ac4EEReRzPCM4JS66qqngk3s1f4j1T96U7TihE2Swp9IPXFdIi5X+iebU9Xs3llMRy4AbgHv9/FZ5lf5YCujMKkHuv//9GvPGEewuRW6DsRBfOT/Ga2g+FXOYWeH27d6Y5RkEeSEVc5wvx+rofjuA6/wa4w/+/B/BbvIe6oO6MQsvsL+sDfJ8CukMLKPMf8LpGf4XnVPBAIWUu4r4+Gs+B41kK9ChsLLvRuoQezwLPpC7HaxncCvwPXg9A6HL9ZdsB/fG6zwLrjm1W36gEFXgRzwRG4NXKWqUsl8R/4FpgRMq6a9nW17s9BfQvFyH3t/7vU4ADIi7zTf7vw0kZV4lA7n/5v1tRwMspoDIfTEp/e1RyE9u6uLf9/60jlPu7lP95v5QDuMa7AZ1CONctEvev/12FNwflADx31OMLvc4ByW1bSJmL+UQqLMcTmfBDvxPPtfK7jda3TPm9p//dH695dRKeH/dvHcn9Xb5yXcp2da6tzCV5b5eM3EJkk1ITx+uqq8frujnWgdwx+coN8uNEaI4n93b87hj//8n+yU5Y2AfxBpy6AvsCV+O5cf2hFOVama3M5VrmEjjXg/DGAQU4H5hNgRU813KD+MQmn4KI3IA30v+Fqj4lInuwbXDlGLzBnZXAJ3gpO28DblfVlSnH2E69yVSxl+tStpXZylyOcoOQLSIHAEvVmywYe7mh4Noq+UbpcmA4nkvjQOAWoB3eAOLLeH3GAlyAd0I7p+xb8LRzV3KtzFbmci1zCZ/rgsOzuJIb1se5Av6JeRG4yP/dA2+Uv6//f6eU7fb1T3Jn/3+xk9CcyLUyW5nLtcx2rqMtcxgfp/MUGvning+gqtV4cYO6iciJqro+ZZdL8WYLJyZoFdT35UquS9lWZitzOcp1KdtlmcMkUqMgXpjlZAwP3TbhZijQQkRO8f9PwJtC39nf/nsiMhbP0l6j/oSluMt1KdvKbGUuR7kuZbssc5REYhRE5HgReRr4jYjskrCQItLK32Q6XuiCH4pIS/XCFnRiW9iAacDVqnqpqi6Ju1wrs5W5XMts5zraMrsgdKMgIqfihYHtj2c5bxaRsyAZJwi82b+D8SabPSBePJ32eHFOUNXxqvp5Kci1MluZy7XMdq6jLbMromgpHIMXi/0V4C48C/pj8eKyIyJ34cUXWY03lbw93glejRfhtNTkupRtZbYyl6Ncl7JdltkJrZrfJD9E5DhghapO8xdNBbqLSGdVXSgi6/CmrF8oIgPw+tn6qupMf/+f443Y55WLwJVcK7OVuVzLbOc62jLHhcBaCiLSTkTew5uccbFsSyA/DS8xxvMi8jpe9MwxwC6qOk1Ve6vqTPFH8tXLMpXzCXUl18psZS7XMtu5jrbMcSOwGc3iZeD6Ht4JPAgYrKrv++u2w8sr20lVXxWRc4Ffqur5/voWWmDoX1dyrcxW5nIts53raMscN4oyCiJyKV7s+i9VdY14+VVb4IUTFrycsgvT7HcLXu7TR0tJrkvZVmYrcznKdSnbZZnjTN7dR+Kxp3j9aZcBPwGeEJGOqrpJVTfgpWNsj5eAOnXfk0RkFF5wqHdLQa6V2cpcrmW2cx1tmUsGzW86d0v/+0DgpcQyvPSEbzTa9jd4o/Vt8ad647l0nZuPTJdyrcxW5nIts53raMtcSp+cTyZeDuL7gVOBbwMvpKxvASwGTk1ZtjNeJqyRwBIKSGLvSq6V2cpcrmW2cx1tmUvxk8sJPRVvtP0J4Eq8GOBnA3OBninbXQ0MSPn/Q2ALXm7k3Qu4kE7kWpmtzOVaZjvX0Za5VD+5nNSTgUtS/j8OXIMXLnaUv6wFXr7af+KngsQLE3tKwYo5kmtltjKXa5ntXEdb5lL95HJSd8Sbvp3oj/sJ2xLUjwGu93/3AF4JTDFHcq3MVuZyLbOd62jLXKqfZr2PVHWDqm5W1Tp/0ZlAjf/7Z8AhIvIu8AowGrwR/uaOG1e5LmVbmaOT61J2pcl1KdtlmUuWPCxuS7xm1gfA/v6y/fEyDJ0EdAnDarmSa2W2Mpdrme1cR1vmUvvkM0+hHi/x9DLgCN+63grUq+oQVV2Qx7HywZVcl7KtzFbmcpTrUrbLMpcWeVrb4/BO7hDgiqgslyu5VmYrs8ktH9kuy1xKn7zCXIjIXsAlQD9V3ZzzjkXiSq5L2VZmK3M5ynUp22WZS4nAAuIZhmEYpU9gobMNwzCM0seMgmEYhpHEjIJhGIaRxIyCYRiGkcSMgmEYhpHEjIJhFICI9BKREwrYb46IdBQvJ/AvU5Z3FpF/BaulYeSPGQWj4hGRVgXs1gvI2yik0A5IGgVVXaiq3y/ieIYRCIU8DIZRcoiXj/cmQIFxQB2wCTgKGCoijwGPAVXABuBKVZ0iIt8GbgG2A5bjRdncAS/+fp2I/BS4HpgC/BX4mi/y16o6VER2wwu21gX4HC/3L8B9wH4iMgb4xJf9rqoeLiKXAxcCOwEHAA/48i8BNuNl/1ohIvul0znA02ZUIq6nVNvHPmF/gMOAaUBH/38H4Hm8PLuJkMqfAgf4v78B9Pd/t2fbJM9fAA/6v28HbkqR8XfgJP/314DJ/u9HgNv83+fhGaWOQFdgQsr+yf94sf5nALvgvfBXA1f76x7CMzgZdbaPfYr5WEvBqAROA15T1WUA6tWy8ZfVicjOeF1Br6VETd7e/94L+IeI7IlXW5+dQcYZwKEp++/qH/cU4Lu+3PdEZGWOOg9Q1bXAWhFZDbzjLx+PF9Atm86GUTBmFIxKZr3/3QJYpard02zzF7xYOW+LSC+8FkI6WgDHqeqm1IVFhOZPjc1Tn/K/Hu+5zaazYRSMDTQblUB/4Ad+/z4i0iF1paquAWaLyA/89SIiR/qr2wKJsMqXpey2Fq97J8HHeGML+Mfo7v8cBPT2l52D1x2Vbv+8aEZnwygYMwpG2aOqE4G7gYEiMhbol2aznwBX+Osn4uXoBa9l8JqIjMKLxZ/gHeAiERkjIicDNwA9RGSciEzCG4gGuAM4RUQm4nUjzfV1Wo43wD1BRP5UYNEy6WwYBWNRUg3DMIwk1lIwDMMwkphRMAzDMJKYUTAMwzCSmFEwDMMwkphRMAzDMJKYUTAMwzCSmFEwDMMwkvw/WfZ2jk+ehAEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1':'2019-5-10']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#每天的情况差不多\n",
    "# next weekend\n",
    "df['weekday']=df.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>createdtime</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdtime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdtime                                                            \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg          createdtime  weekday  \n",
       "createdtime                                                      \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>createdtime</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdtime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "createdtime                                                            \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg          createdtime  weekday  weekend  \n",
       "createdtime                                                               \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3    False  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3    False  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3    False  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] =df['weekday'].isin({5,6})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#周末调用的平均次数多 \n",
    "#查看周末哪个时段的调用次数多\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  createdtime\n",
       "False    0               3.239120\n",
       "         1               1.668388\n",
       "         2               1.162551\n",
       "         3               1.086705\n",
       "         4               1.155556\n",
       "         5               1.136364\n",
       "         6               1.000000\n",
       "         7               1.000000\n",
       "         8               1.000000\n",
       "         9               1.080000\n",
       "         10              1.239011\n",
       "         11              2.031690\n",
       "         12              4.195845\n",
       "         13              6.668042\n",
       "         14              8.260503\n",
       "         15              8.934448\n",
       "         16              8.466504\n",
       "         17              6.784996\n",
       "         18              6.717731\n",
       "         19              8.655913\n",
       "         20             10.536496\n",
       "         21             10.846906\n",
       "         22              9.034164\n",
       "         23              5.946834\n",
       "True     0               3.467782\n",
       "         1               1.741849\n",
       "         2               1.161826\n",
       "         3               1.050000\n",
       "         4               1.076923\n",
       "         5               1.333333\n",
       "         6               1.000000\n",
       "         7               1.000000\n",
       "         8               1.071429\n",
       "         9               1.144928\n",
       "         10              1.254111\n",
       "         11              1.992958\n",
       "         12              4.031889\n",
       "         13              6.905772\n",
       "         14              8.851321\n",
       "         15              9.858422\n",
       "         16              9.420550\n",
       "         17              7.334743\n",
       "         18              7.342150\n",
       "         19              9.270430\n",
       "         20             11.173609\n",
       "         21             11.695043\n",
       "         22             10.419916\n",
       "         23              7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 周末和非周末 绘制成 图形， 否则不直观\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>createdtime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend          False      True \n",
       "createdtime                      \n",
       "0             3.239120   3.467782\n",
       "1             1.668388   1.741849\n",
       "2             1.162551   1.161826\n",
       "3             1.086705   1.050000\n",
       "4             1.155556   1.076923\n",
       "5             1.136364   1.333333\n",
       "6             1.000000   1.000000\n",
       "7             1.000000   1.000000\n",
       "8             1.000000   1.071429\n",
       "9             1.080000   1.144928\n",
       "10            1.239011   1.254111\n",
       "11            2.031690   1.992958\n",
       "12            4.195845   4.031889\n",
       "13            6.668042   6.905772\n",
       "14            8.260503   8.851321\n",
       "15            8.934448   9.858422\n",
       "16            8.466504   9.420550\n",
       "17            6.784996   7.334743\n",
       "18            6.717731   7.342150\n",
       "19            8.655913   9.270430\n",
       "20           10.536496  11.173609\n",
       "21           10.846906  11.695043\n",
       "22            9.034164  10.419916\n",
       "23            5.946834   7.025452"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level=0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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